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Question

Consider the following data for the next three (03) items that follow :

There are 90 applicants for a job. Some of them are graduates. Some of them have less than three years experience.

Number of graduatesNumber of non-graduates
At least 3 years experience189
Less than 3 years experience3627

Let G be the event that the first applicant interviewed is a graduate and T be the event that first applicant interviewed has at least 3 years experience.

What is \(P (G \cap \overline T)\) equal to?

This question was previously asked in
NDA I 2022 GAT Previous Year Paper (10-Apr-2022)
The correct answer is \(\frac{2}{5}\)

Calculating Probability for Job Applicants

The problem provides data about 90 job applicants, categorized by whether they are graduates and their years of experience. We are given the following data in a table format:

At least 3 years experience (T) Less than 3 years experience (\(\overline T\)) Total
Number of graduates (G) 18 36 54
Number of non-graduates (\(\overline G\)) 9 27 36
Total 27 63 90

We are asked to find the probability \(P(G \cap \overline T)\). This represents the probability that the first applicant interviewed is a graduate (event G) AND has less than 3 years experience (event \(\overline T\)). The symbol \(\cap\) denotes the intersection of the events, meaning both events occur.

Understanding \(P(G \cap \overline T)\)

The term \(G \cap \overline T\) refers to the set of applicants who are both graduates and have less than 3 years of experience. Looking at the provided table:

  • The row "Number of graduates (G)" gives data for graduates.
  • The column "Less than 3 years experience (\(\overline T\))" gives data for those with less than 3 years experience.
  • The cell where this row and column intersect shows the number of applicants who satisfy both conditions.

From the table, the number of graduates with less than 3 years experience is 36.

Calculating the Probability

The probability of an event is calculated as the ratio of the number of favorable outcomes to the total number of possible outcomes.

In this case:

  • Favorable outcomes: Applicants who are graduates AND have less than 3 years experience. Number of favorable outcomes = 36.
  • Total possible outcomes: All the applicants. Total number of applicants = 90.

The probability \(P(G \cap \overline T)\) is therefore:

\(P(G \cap \overline T) = \frac{\text{Number of graduates with less than 3 years experience}}{\text{Total number of applicants}}\)

\(P(G \cap \overline T) = \frac{36}{90}\)

Simplifying the Fraction

To simplify the fraction \(\frac{36}{90}\), we can find the greatest common divisor (GCD) of 36 and 90, or simply divide by common factors:

  • Divide both numerator and denominator by 2: \(\frac{36 \div 2}{90 \div 2} = \frac{18}{45}\)
  • Divide both numerator and denominator by 9: \(\frac{18 \div 9}{45 \div 9} = \frac{2}{5}\)

So, \(P(G \cap \overline T) = \frac{2}{5}\).

Conclusion

The probability that the first applicant interviewed is a graduate and has less than 3 years experience is \(\frac{2}{5}\).

Revision Table: Probability of Job Applicant Characteristics

Event Description Number of Applicants Probability (Number/90)
G Graduate 54 \(\frac{54}{90} = \frac{3}{5}\)
\(\overline G\) Non-graduate 36 \(\frac{36}{90} = \frac{2}{5}\)
T At least 3 years experience 27 \(\frac{27}{90} = \frac{3}{10}\)
\(\overline T\) Less than 3 years experience 63 \(\frac{63}{90} = \frac{7}{10}\)
\(G \cap T\) Graduate and At least 3 years experience 18 \(\frac{18}{90} = \frac{1}{5}\)
\(G \cap \overline T\) Graduate and Less than 3 years experience 36 \(\frac{36}{90} = \frac{2}{5}\)
\(\overline G \cap T\) Non-graduate and At least 3 years experience 9 \(\frac{9}{90} = \frac{1}{10}\)
\(\overline G \cap \overline T\) Non-graduate and Less than 3 years experience 27 \(\frac{27}{90} = \frac{3}{10}\)

Additional Information: Contingency Tables and Joint Probability

The table provided is a type of contingency table, specifically a 2x2 table, used to display the frequency distribution of two variables (in this case, Graduate Status and Experience). Such tables are very useful in calculating probabilities, especially joint probabilities and marginal probabilities.

  • Joint Probability: This is the probability of two events occurring together. \(P(A \cap B)\) is a joint probability. In our case, \(P(G \cap \overline T)\) is a joint probability. The numerator comes directly from the cells within the table (like 36).
  • Marginal Probability: This is the probability of a single event occurring, regardless of the outcome of the other variable. Marginal probabilities are calculated using the row or column totals (the "margins"). For example, \(P(G)\) is the total number of graduates divided by the total applicants (\(54/90\)).
  • Conditional Probability: Although not asked in this question, contingency tables are also used to calculate conditional probabilities, like \(P(G | \overline T)\) (the probability of being a graduate G, given that the applicant has less than 3 years experience \(\overline T\)). This would be calculated as \(\frac{P(G \cap \overline T)}{P(\overline T)}\) or directly from the table as \(\frac{\text{Number of } G \cap \overline T}{\text{Total number of } \overline T}\), which is \(\frac{36}{63}\).

Understanding how to read and use contingency tables is fundamental for solving probability problems involving two or more categorical variables.

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Similar Questions

  1. A and B are two events such that A̅ and B̅ are mutually exclusive. If P(A) = 0.5 and P(B) = 0.6, then what is the value of P(A|B)?

  2. For two dependent events A and B, it is given that P(A) = 0.2 and P(B) = 0.5. If A ⊆ B, then the values of conditional probabilities P(A|B) and P(B|A) are respectively

  3. If two dice are thrown and at least one the dice show 5, then the probability that the sum is 10 or more is

  4. If A and B are two events such that P(A) = 0.6, P(B) = 0.5 and P(A ∩ B) = 0.4, then consider the following statements:

    1. P(A̅ ∪ B) = 0.9

    2. P(B̅ | A̅) = 0.6

    Which of the statements is / are correct? 

  5. For two mutually exclusive events A and B, P(A) = 0.2 and P (A̅ ∩ B) = 0.3. What is P (A|(A ∪ B)) equal to?

  6. Consider the following statements:

    1. If A and B are mutually exclusive events, then it is possible that P(A) = P(B) = 0.6.

    2. If A and B are any two events such that P(A|B) = 1, then P(B̅|A̅) = 1

    Which of the above statement is/are correct?
  7. If A, B, C are three events, then what is the probability that at least two of these events occur together?

  8. If 5 of a Company’s 10 delivery trucks do not meet emission standards and 3 of them are chosen for inspection, then what is the probability that none of the trucks chosen will meet emission standards?

  9. A problem is given to three students A, B and C whose probabilities of solving the problem are \(\frac{1}{2},\frac{3}{4}\) and \(\frac{1}{4}\)  respectively. What is the probability that the problem will be solved if they all solve the problem independently?

  10. Three groups of children contain 3 girls and 1 boy; 2 girls and 2 boys: 1 girl and 3 boys. One child is selected at random from each group. The probability that the three selected consist of 1 girl and 2 boys is


Important Questions from Conditional Probability

  1. A and B are two events such that A̅ and B̅ are mutually exclusive. If P(A) = 0.5 and P(B) = 0.6, then what is the value of P(A|B)?

  2. For two dependent events A and B, it is given that P(A) = 0.2 and P(B) = 0.5. If A ⊆ B, then the values of conditional probabilities P(A|B) and P(B|A) are respectively

  3. In a bulb factory, machines P, Q and R manufacture respectively 25%, 35% and 40% of the total. Of their output 5, 4 and 2 percent respectively are defective bulbs. A bulb is drawn at random and it is found to be defective. What is the probability that it was manufactured by machine Q?

  4. Let X1 and X2 be independent random variables each having geometric distribution qk p ; k = 0, 1, 2, …. Then the conditional distribution of X1 given X1 + X2 is

  5. Find the probability that they speak the same fact if A speaks truth in 60% cases and B speaks truth in 75% cases?

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